dplyr 0.4.3 includes over 30 minor improvements and bug fixes, which are described in detail in the release notes. Here I wanted to draw your attention five small, but important, changes:
mutate() no longer randomly crashes! (Sorry it took us so long to fix this - I know it’s been causing a lot of pain.)
dplyr now has much better support for non-ASCII column names. It’s probably not perfect, but should be a lot better than previous versions.
When printing a
tbl_df, you now see the types of all columns, not just those that don’t fit on the screen:
data_frame(x = 1:3, y = letters[x], z = factor(y)) #> Source: local data frame [3 x 3] #> #> x y z #> (int) (chr) (fctr) #> 1 1 a a #> 2 2 b b #> 3 3 c c
.idargument. When supplied, it creates a new column that gives the name of each data frame:
a <- data_frame(x = 1, y = "a") b <- data_frame(x = 2, y = "c") bind_rows(a = a, b = b) #> Source: local data frame [2 x 2] #> #> x y #> (dbl) (chr) #> 1 1 a #> 2 2 c bind_rows(a = a, b = b, .id = "source") #> Source: local data frame [2 x 3] #> #> source x y #> (chr) (dbl) (chr) #> 1 a 1 a #> 2 b 2 c # Or equivalently bind_rows(list(a = a, b = b), .id = "source") #> Source: local data frame [2 x 3] #> #> source x y #> (chr) (dbl) (chr) #> 1 a 1 a #> 2 b 2 c
summarise()preserve attributes of the data frame itself.
We are excited to announce real-time collaborative editing on RStudio Cloud. Users can join the same project, edit code, and immediately see each other’s changes.
In this series, we walk through lesser-known tips and tricks to help you work more effectively and efficiently in R Markdown. This third post focuses on features that save you time and trouble.